Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations32676
Missing cells210
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 MiB
Average record size in memory661.9 B

Variable types

Categorical6
Text3
Numeric7

Alerts

Act is highly overall correlated with Axi and 2 other fieldsHigh correlation
Asignatura is highly overall correlated with Intensidad_HorariaHigh correlation
Axi is highly overall correlated with Act and 1 other fieldsHigh correlation
Cog is highly overall correlated with Nivel and 2 other fieldsHigh correlation
Intensidad_Horaria is highly overall correlated with AsignaturaHigh correlation
Nivel is highly overall correlated with Cog and 2 other fieldsHigh correlation
Proc is highly overall correlated with Act and 3 other fieldsHigh correlation
Resultado is highly overall correlated with Act and 4 other fieldsHigh correlation
Grupo is highly imbalanced (82.0%) Imbalance

Reproduction

Analysis started2025-05-26 01:36:56.400237
Analysis finished2025-05-26 01:37:03.009542
Duration6.61 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Sede
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
FUSAGASUGÁ
21018 
GIRARDOT
11658 

Length

Max length10
Median length10
Mean length9.2864488
Min length8

Characters and Unicode

Total characters303444
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFUSAGASUGÁ
2nd rowFUSAGASUGÁ
3rd rowFUSAGASUGÁ
4th rowFUSAGASUGÁ
5th rowFUSAGASUGÁ

Common Values

ValueCountFrequency (%)
FUSAGASUGÁ 21018
64.3%
GIRARDOT 11658
35.7%

Length

2025-05-25T20:37:03.103812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T20:37:03.178391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fusagasugá 21018
64.3%
girardot 11658
35.7%

Most occurring characters

ValueCountFrequency (%)
A 53694
17.7%
G 53694
17.7%
U 42036
13.9%
S 42036
13.9%
R 23316
7.7%
F 21018
 
6.9%
Á 21018
 
6.9%
I 11658
 
3.8%
D 11658
 
3.8%
O 11658
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 303444
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 53694
17.7%
G 53694
17.7%
U 42036
13.9%
S 42036
13.9%
R 23316
7.7%
F 21018
 
6.9%
Á 21018
 
6.9%
I 11658
 
3.8%
D 11658
 
3.8%
O 11658
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 303444
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 53694
17.7%
G 53694
17.7%
U 42036
13.9%
S 42036
13.9%
R 23316
7.7%
F 21018
 
6.9%
Á 21018
 
6.9%
I 11658
 
3.8%
D 11658
 
3.8%
O 11658
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 282426
93.1%
None 21018
 
6.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 53694
19.0%
G 53694
19.0%
U 42036
14.9%
S 42036
14.9%
R 23316
8.3%
F 21018
 
7.4%
I 11658
 
4.1%
D 11658
 
4.1%
O 11658
 
4.1%
T 11658
 
4.1%
None
ValueCountFrequency (%)
Á 21018
100.0%
Distinct536
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-05-25T20:37:03.355004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters2091264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
2nd row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
3rd row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
4th row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
5th row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
ValueCountFrequency (%)
6e2ed213a16fd82c2eaa6a7f60ac88355973c844033b2587d01a47428c5ff94a 114
 
0.3%
f9d935cfdd65b86ab48b8744e498b948f967135a90a5b745123143c179e71953 114
 
0.3%
ff141b8ae5959a35409bcc4e5d44b303a1981ac691a6fffe2fe29d716209226d 114
 
0.3%
9519826a81aaa636f1e893349c83c1039cf7d753d244486d09a92b34305c2d3c 114
 
0.3%
5b0b2f629983dcb33cf57fdd482995d078d564763f3aa7cbce449fa9f92aecd9 114
 
0.3%
ebf9e2f6852a5df378d9a51de531bdc5a23c21726d5351b2d45d57bb7f119c53 114
 
0.3%
b603b2fbee9c95020d8c2068d1bb49a31818e9041052e9daa822ce3171bd6e95 114
 
0.3%
67e962b5d7a5d8acf0bf8d3ad7b649731fb5b93a572701cb179e7ad34085913f 114
 
0.3%
ea2800df56fb0d136863b14274c1ee624f87a3b2ed4f526f12378bb008d5c9a5 114
 
0.3%
d6cb47c4a8c6847e4150ff2b10b72f233da00b1661acd33407a234bf22a405ef 110
 
0.3%
Other values (526) 31540
96.5%
2025-05-25T20:37:03.638662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 136766
 
6.5%
2 135579
 
6.5%
1 135130
 
6.5%
5 133563
 
6.4%
d 131742
 
6.3%
8 131615
 
6.3%
3 131551
 
6.3%
b 130427
 
6.2%
a 129890
 
6.2%
f 129731
 
6.2%
Other values (6) 765270
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1304834
62.4%
Lowercase Letter 786430
37.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 135579
10.4%
1 135130
10.4%
5 133563
10.2%
8 131615
10.1%
3 131551
10.1%
7 128505
9.8%
0 128425
9.8%
4 128126
9.8%
9 127403
9.8%
6 124937
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 136766
17.4%
d 131742
16.8%
b 130427
16.6%
a 129890
16.5%
f 129731
16.5%
e 127874
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1304834
62.4%
Latin 786430
37.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 135579
10.4%
1 135130
10.4%
5 133563
10.2%
8 131615
10.1%
3 131551
10.1%
7 128505
9.8%
0 128425
9.8%
4 128126
9.8%
9 127403
9.8%
6 124937
9.6%
Latin
ValueCountFrequency (%)
c 136766
17.4%
d 131742
16.8%
b 130427
16.6%
a 129890
16.5%
f 129731
16.5%
e 127874
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2091264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 136766
 
6.5%
2 135579
 
6.5%
1 135130
 
6.5%
5 133563
 
6.4%
d 131742
 
6.3%
8 131615
 
6.3%
3 131551
 
6.3%
b 130427
 
6.2%
a 129890
 
6.2%
f 129731
 
6.2%
Other values (6) 765270
36.6%

Grado
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2058085
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-05-25T20:37:03.723652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7482536
Coefficient of variation (CV)0.52792061
Kurtosis-0.8688528
Mean5.2058085
Median Absolute Deviation (MAD)2
Skewness0.23031629
Sum170105
Variance7.5528979
MonotonicityNot monotonic
2025-05-25T20:37:03.796074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6 4047
12.4%
4 3933
12.0%
3 3760
11.5%
7 3712
11.4%
5 3664
11.2%
2 3258
10.0%
1 3221
9.9%
8 2534
7.8%
10 1859
5.7%
9 1830
5.6%
ValueCountFrequency (%)
1 3221
9.9%
2 3258
10.0%
3 3760
11.5%
4 3933
12.0%
5 3664
11.2%
6 4047
12.4%
7 3712
11.4%
8 2534
7.8%
9 1830
5.6%
10 1859
5.7%
ValueCountFrequency (%)
11 858
 
2.6%
10 1859
5.7%
9 1830
5.6%
8 2534
7.8%
7 3712
11.4%
6 4047
12.4%
5 3664
11.2%
4 3933
12.0%
3 3760
11.5%
2 3258
10.0%

Grupo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
A
31787 
B
 
889

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 31787
97.3%
B 889
 
2.7%

Length

2025-05-25T20:37:03.882303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T20:37:03.941269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 31787
97.3%
b 889
 
2.7%

Most occurring characters

ValueCountFrequency (%)
A 31787
97.3%
B 889
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 32676
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 31787
97.3%
B 889
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 32676
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 31787
97.3%
B 889
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 31787
97.3%
B 889
 
2.7%

Periodo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1
10836 
2
7368 
4
7262 
3
7210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32676
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 10836
33.2%
2 7368
22.5%
4 7262
22.2%
3 7210
22.1%

Length

2025-05-25T20:37:04.008948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T20:37:04.077247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10836
33.2%
2 7368
22.5%
4 7262
22.2%
3 7210
22.1%

Most occurring characters

ValueCountFrequency (%)
1 10836
33.2%
2 7368
22.5%
4 7262
22.2%
3 7210
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10836
33.2%
2 7368
22.5%
4 7262
22.2%
3 7210
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10836
33.2%
2 7368
22.5%
4 7262
22.2%
3 7210
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10836
33.2%
2 7368
22.5%
4 7262
22.2%
3 7210
22.1%

Año
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024
14716 
2023
14547 
2025
3413 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters130704
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2024 14716
45.0%
2023 14547
44.5%
2025 3413
 
10.4%

Length

2025-05-25T20:37:04.224756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T20:37:04.317586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2024 14716
45.0%
2023 14547
44.5%
2025 3413
 
10.4%

Most occurring characters

ValueCountFrequency (%)
2 65352
50.0%
0 32676
25.0%
4 14716
 
11.3%
3 14547
 
11.1%
5 3413
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130704
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 65352
50.0%
0 32676
25.0%
4 14716
 
11.3%
3 14547
 
11.1%
5 3413
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 130704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 65352
50.0%
0 32676
25.0%
4 14716
 
11.3%
3 14547
 
11.1%
5 3413
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 65352
50.0%
0 32676
25.0%
4 14716
 
11.3%
3 14547
 
11.1%
5 3413
 
2.6%

Intensidad_Horaria
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7389827
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-05-25T20:37:04.377407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5046857
Coefficient of variation (CV)0.54935932
Kurtosis-0.23432131
Mean2.7389827
Median Absolute Deviation (MAD)1
Skewness0.60498164
Sum89499
Variance2.2640791
MonotonicityNot monotonic
2025-05-25T20:37:04.447056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 9130
27.9%
3 7090
21.7%
4 6549
20.0%
2 6299
19.3%
5 1883
 
5.8%
6 1185
 
3.6%
7 540
 
1.7%
ValueCountFrequency (%)
1 9130
27.9%
2 6299
19.3%
3 7090
21.7%
4 6549
20.0%
5 1883
 
5.8%
6 1185
 
3.6%
7 540
 
1.7%
ValueCountFrequency (%)
7 540
 
1.7%
6 1185
 
3.6%
5 1883
 
5.8%
4 6549
20.0%
3 7090
21.7%
2 6299
19.3%
1 9130
27.9%

Asignatura
Categorical

High correlation 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
EDUCACIÓN FÍSICA
2710 
CIENCIAS NATURALES
2706 
LECTURA CRÍTICA
2702 
LENGUA CASTELLANA
2701 
INGLÉS
2701 
Other values (20)
19156 

Length

Max length37
Median length24
Mean length17.138358
Min length5

Characters and Unicode

Total characters560013
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCIENCIAS NATURALES
2nd rowCIENCIAS SOCIALES
3rd rowMATEMÁTICAS
4th rowLENGUA CASTELLANA
5th rowINGLÉS

Common Values

ValueCountFrequency (%)
EDUCACIÓN FÍSICA 2710
 
8.3%
CIENCIAS NATURALES 2706
 
8.3%
LECTURA CRÍTICA 2702
 
8.3%
LENGUA CASTELLANA 2701
 
8.3%
INGLÉS 2701
 
8.3%
CIENCIAS SOCIALES 2646
 
8.1%
MATEMÁTICAS 2643
 
8.1%
ARTES 2419
 
7.4%
TECNOLOGÍAS INFORMÁTICAS 1498
 
4.6%
INTEGRALIDAD 1493
 
4.6%
Other values (15) 8457
25.9%

Length

2025-05-25T20:37:04.546508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ciencias 5414
 
8.0%
educación 2771
 
4.1%
física 2737
 
4.0%
naturales 2706
 
4.0%
lectura 2702
 
4.0%
crítica 2702
 
4.0%
lengua 2701
 
4.0%
castellana 2701
 
4.0%
inglés 2701
 
4.0%
integralidad 2699
 
4.0%
Other values (32) 37817
55.9%

Most occurring characters

ValueCountFrequency (%)
A 72444
12.9%
I 59595
10.6%
E 53197
9.5%
C 50458
9.0%
N 46270
 
8.3%
S 37053
 
6.6%
34975
 
6.2%
T 34246
 
6.1%
L 25945
 
4.6%
R 23782
 
4.2%
Other values (16) 122048
21.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 525038
93.8%
Space Separator 34975
 
6.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 72444
13.8%
I 59595
11.4%
E 53197
10.1%
C 50458
9.6%
N 46270
8.8%
S 37053
 
7.1%
T 34246
 
6.5%
L 25945
 
4.9%
R 23782
 
4.5%
O 21071
 
4.0%
Other values (15) 100977
19.2%
Space Separator
ValueCountFrequency (%)
34975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 525038
93.8%
Common 34975
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 72444
13.8%
I 59595
11.4%
E 53197
10.1%
C 50458
9.6%
N 46270
8.8%
S 37053
 
7.1%
T 34246
 
6.5%
L 25945
 
4.9%
R 23782
 
4.5%
O 21071
 
4.0%
Other values (15) 100977
19.2%
Common
ValueCountFrequency (%)
34975
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 537444
96.0%
None 22569
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 72444
13.5%
I 59595
11.1%
E 53197
9.9%
C 50458
9.4%
N 46270
8.6%
S 37053
 
6.9%
34975
 
6.5%
T 34246
 
6.4%
L 25945
 
4.8%
R 23782
 
4.4%
Other values (12) 99479
18.5%
None
ValueCountFrequency (%)
Ó 7756
34.4%
Í 7545
33.4%
Á 4141
18.3%
É 3127
13.9%

Cog
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)0.3%
Missing105
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean84.03325
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-05-25T20:37:04.658829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile66
Q179
median86
Q391
95-th percentile96
Maximum100
Range93
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.6033828
Coefficient of variation (CV)0.11428075
Kurtosis2.9045286
Mean84.03325
Median Absolute Deviation (MAD)6
Skewness-1.2460093
Sum2737047
Variance92.224961
MonotonicityNot monotonic
2025-05-25T20:37:04.781711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 1890
 
5.8%
90 1729
 
5.3%
91 1645
 
5.0%
92 1582
 
4.8%
87 1560
 
4.8%
86 1445
 
4.4%
84 1377
 
4.2%
88 1374
 
4.2%
94 1351
 
4.1%
85 1347
 
4.1%
Other values (73) 17271
52.9%
ValueCountFrequency (%)
7 1
< 0.1%
8 2
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
15 2
< 0.1%
16 1
< 0.1%
22 2
< 0.1%
23 2
< 0.1%
25 2
< 0.1%
26 2
< 0.1%
ValueCountFrequency (%)
100 321
 
1.0%
99 349
 
1.1%
98 227
 
0.7%
97 567
 
1.7%
96 652
 
2.0%
95 862
2.6%
94 1351
4.1%
93 1129
3.5%
92 1582
4.8%
91 1645
5.0%

Proc
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)0.3%
Missing105
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean83.779743
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-05-25T20:37:04.900231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile65
Q179
median86
Q391
95-th percentile96
Maximum100
Range93
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.031476
Coefficient of variation (CV)0.1197363
Kurtosis2.9691249
Mean83.779743
Median Absolute Deviation (MAD)6
Skewness-1.3209056
Sum2728790
Variance100.63052
MonotonicityNot monotonic
2025-05-25T20:37:05.042343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 1787
 
5.5%
90 1754
 
5.4%
91 1704
 
5.2%
87 1647
 
5.0%
92 1608
 
4.9%
84 1405
 
4.3%
88 1401
 
4.3%
86 1388
 
4.2%
94 1325
 
4.1%
85 1323
 
4.0%
Other values (76) 17229
52.7%
ValueCountFrequency (%)
7 1
< 0.1%
8 2
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 2
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
22 2
< 0.1%
23 1
< 0.1%
24 1
< 0.1%
ValueCountFrequency (%)
100 277
 
0.8%
99 293
 
0.9%
98 217
 
0.7%
97 633
 
1.9%
96 659
 
2.0%
95 942
2.9%
94 1325
4.1%
93 1094
3.3%
92 1608
4.9%
91 1704
5.2%

Act
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.15975
Minimum0
Maximum100
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-05-25T20:37:05.166504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q185
median90
Q395
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9954645
Coefficient of variation (CV)0.10203596
Kurtosis10.855837
Mean88.15975
Median Absolute Deviation (MAD)5
Skewness-2.0721111
Sum2880708
Variance80.918381
MonotonicityNot monotonic
2025-05-25T20:37:05.284518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 7250
22.2%
95 3748
11.5%
80 3611
11.1%
100 2967
 
9.1%
85 2684
 
8.2%
88 1083
 
3.3%
87 958
 
2.9%
92 932
 
2.9%
70 879
 
2.7%
89 849
 
2.6%
Other values (60) 7715
23.6%
ValueCountFrequency (%)
0 22
0.1%
8 1
 
< 0.1%
10 3
 
< 0.1%
15 2
 
< 0.1%
20 5
 
< 0.1%
23 1
 
< 0.1%
25 3
 
< 0.1%
26 1
 
< 0.1%
30 13
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
100 2967
9.1%
99 219
 
0.7%
98 615
 
1.9%
97 510
 
1.6%
96 592
 
1.8%
95 3748
11.5%
94 729
 
2.2%
93 848
 
2.6%
92 932
 
2.9%
91 377
 
1.2%

Axi
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.829722
Minimum0
Maximum100
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-05-25T20:37:05.401108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q187
median90
Q395
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.3199571
Coefficient of variation (CV)0.092619201
Kurtosis13.417228
Mean89.829722
Median Absolute Deviation (MAD)5
Skewness-2.3026303
Sum2935276
Variance69.221686
MonotonicityNot monotonic
2025-05-25T20:37:05.692561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 7698
23.6%
95 4658
14.3%
100 3870
11.8%
80 2654
 
8.1%
85 2015
 
6.2%
93 984
 
3.0%
92 965
 
3.0%
96 944
 
2.9%
88 929
 
2.8%
89 851
 
2.6%
Other values (57) 7108
21.8%
ValueCountFrequency (%)
0 16
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 5
 
< 0.1%
19 1
 
< 0.1%
20 5
 
< 0.1%
25 3
 
< 0.1%
30 11
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
ValueCountFrequency (%)
100 3870
11.8%
99 297
 
0.9%
98 798
 
2.4%
97 559
 
1.7%
96 944
 
2.9%
95 4658
14.3%
94 797
 
2.4%
93 984
 
3.0%
92 965
 
3.0%
91 434
 
1.3%
Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2025-05-25T20:37:05.928636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length25
Mean length22.393653
Min length3

Characters and Unicode

Total characters731735
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALEJANDRA LEÓN DICELIS
2nd rowKATHERINE CUBILLOS VÉLEZ
3rd rowTATIANA GONZÁLEZ MORENO
4th rowKATHERINE CUBILLOS VÉLEZ
5th rowALEJANDRA MORALES GARCÍA
ValueCountFrequency (%)
sánchez 3051
 
3.1%
alejandra 3029
 
3.1%
rodríguez 2514
 
2.6%
rincón 2033
 
2.1%
stivenson 1978
 
2.0%
najas 1978
 
2.0%
torres 1723
 
1.8%
barrero 1712
 
1.7%
steven 1712
 
1.7%
buitrago 1712
 
1.7%
Other values (152) 76554
78.1%
2025-05-25T20:37:06.254408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 106376
14.5%
65320
 
8.9%
R 62638
 
8.6%
E 56915
 
7.8%
N 56227
 
7.7%
O 50439
 
6.9%
I 43780
 
6.0%
L 41352
 
5.7%
S 39083
 
5.3%
T 32337
 
4.4%
Other values (31) 177268
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 666319
91.1%
Space Separator 65320
 
8.9%
Decimal Number 96
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 106376
16.0%
R 62638
 
9.4%
E 56915
 
8.5%
N 56227
 
8.4%
O 50439
 
7.6%
I 43780
 
6.6%
L 41352
 
6.2%
S 39083
 
5.9%
T 32337
 
4.9%
C 20354
 
3.1%
Other values (21) 156818
23.5%
Decimal Number
ValueCountFrequency (%)
1 32
33.3%
8 8
 
8.3%
9 8
 
8.3%
0 8
 
8.3%
5 8
 
8.3%
4 8
 
8.3%
3 8
 
8.3%
2 8
 
8.3%
7 8
 
8.3%
Space Separator
ValueCountFrequency (%)
65320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 666319
91.1%
Common 65416
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 106376
16.0%
R 62638
 
9.4%
E 56915
 
8.5%
N 56227
 
8.4%
O 50439
 
7.6%
I 43780
 
6.6%
L 41352
 
6.2%
S 39083
 
5.9%
T 32337
 
4.9%
C 20354
 
3.1%
Other values (21) 156818
23.5%
Common
ValueCountFrequency (%)
65320
99.9%
1 32
 
< 0.1%
8 8
 
< 0.1%
9 8
 
< 0.1%
0 8
 
< 0.1%
5 8
 
< 0.1%
4 8
 
< 0.1%
3 8
 
< 0.1%
2 8
 
< 0.1%
7 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 711178
97.2%
None 20557
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 106376
15.0%
65320
9.2%
R 62638
 
8.8%
E 56915
 
8.0%
N 56227
 
7.9%
O 50439
 
7.1%
I 43780
 
6.2%
L 41352
 
5.8%
S 39083
 
5.5%
T 32337
 
4.5%
Other values (25) 156711
22.0%
None
ValueCountFrequency (%)
Á 6805
33.1%
Ó 5904
28.7%
Í 4275
20.8%
É 2362
 
11.5%
Ñ 831
 
4.0%
Ì 380
 
1.8%

Resultado
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.386002
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-05-25T20:37:06.359130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile70
Q181
median87
Q391
95-th percentile96
Maximum100
Range95
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.33133
Coefficient of variation (CV)0.09757255
Kurtosis5.4005545
Mean85.386002
Median Absolute Deviation (MAD)5
Skewness-1.4836644
Sum2790073
Variance69.411059
MonotonicityNot monotonic
2025-05-25T20:37:06.484885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 1943
 
5.9%
90 1943
 
5.9%
91 1789
 
5.5%
87 1757
 
5.4%
88 1730
 
5.3%
86 1723
 
5.3%
92 1627
 
5.0%
85 1604
 
4.9%
84 1522
 
4.7%
93 1435
 
4.4%
Other values (76) 15603
47.8%
ValueCountFrequency (%)
5 1
< 0.1%
6 2
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
17 2
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 2
< 0.1%
ValueCountFrequency (%)
100 216
 
0.7%
99 213
 
0.7%
98 344
 
1.1%
97 501
 
1.5%
96 727
2.2%
95 1106
3.4%
94 1341
4.1%
93 1435
4.4%
92 1627
5.0%
91 1789
5.5%

Nivel
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
ALTO
23290 
BÁSICO
5948 
SUPERIOR
3107 
BAJO
 
331

Length

Max length8
Median length4
Mean length4.7443996
Min length4

Characters and Unicode

Total characters155028
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUPERIOR
2nd rowALTO
3rd rowALTO
4th rowSUPERIOR
5th rowALTO

Common Values

ValueCountFrequency (%)
ALTO 23290
71.3%
BÁSICO 5948
 
18.2%
SUPERIOR 3107
 
9.5%
BAJO 331
 
1.0%

Length

2025-05-25T20:37:06.598776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T20:37:06.672960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
alto 23290
71.3%
básico 5948
 
18.2%
superior 3107
 
9.5%
bajo 331
 
1.0%

Most occurring characters

ValueCountFrequency (%)
O 32676
21.1%
A 23621
15.2%
L 23290
15.0%
T 23290
15.0%
S 9055
 
5.8%
I 9055
 
5.8%
B 6279
 
4.1%
R 6214
 
4.0%
Á 5948
 
3.8%
C 5948
 
3.8%
Other values (4) 9652
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 155028
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 32676
21.1%
A 23621
15.2%
L 23290
15.0%
T 23290
15.0%
S 9055
 
5.8%
I 9055
 
5.8%
B 6279
 
4.1%
R 6214
 
4.0%
Á 5948
 
3.8%
C 5948
 
3.8%
Other values (4) 9652
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 155028
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 32676
21.1%
A 23621
15.2%
L 23290
15.0%
T 23290
15.0%
S 9055
 
5.8%
I 9055
 
5.8%
B 6279
 
4.1%
R 6214
 
4.0%
Á 5948
 
3.8%
C 5948
 
3.8%
Other values (4) 9652
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149080
96.2%
None 5948
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 32676
21.9%
A 23621
15.8%
L 23290
15.6%
T 23290
15.6%
S 9055
 
6.1%
I 9055
 
6.1%
B 6279
 
4.2%
R 6214
 
4.2%
C 5948
 
4.0%
U 3107
 
2.1%
Other values (3) 6545
 
4.4%
None
ValueCountFrequency (%)
Á 5948
100.0%
Distinct464
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-05-25T20:37:06.862188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters2091264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
2nd row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
3rd row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
4th row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
5th row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
ValueCountFrequency (%)
6972dfae4c538733dd215603f7fd81a43f9ac6b0396a56b08aa075b202bb0899 114
 
0.3%
22c7fb72bf379aa7c086a1f4a71a42828dfa0d1bca282a056b2b575cc18ec243 114
 
0.3%
76f410cd43a36a0e832f41a95d4106eb689a389970237347895d5d15831fec84 114
 
0.3%
8a59db949fbfa3448b7baa9846cdff2716e16cf13a27482094b4e5c9da15d132 114
 
0.3%
087e955b29fb14a0939676839f94716a0d484c19c681e6a91ca658a1ed7527dd 114
 
0.3%
884718eb1cb083208033f0496dc85f22cddd8456a43ad721e428862c6ed88a8a 114
 
0.3%
e04b0e98e8ed34aed94d0fd4607b104c1a0e95245d4d1d59457cb6e24b7a2cf5 114
 
0.3%
eff9626bca10271224098b63c922abe6698881efd47cadc3f22ec39a65353330 114
 
0.3%
3713ac9da3dce5378592ca1fdab4ab85430eb45f51e79b3407d67a38199ecf7c 114
 
0.3%
5e6336ba4dc4061a9835b7ca9ff9f7cb3460be9cbfe7df778451f765c4ed8ac5 114
 
0.3%
Other values (454) 31536
96.5%
2025-05-25T20:37:07.147648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 134557
 
6.4%
2 134466
 
6.4%
6 134047
 
6.4%
b 133592
 
6.4%
3 132910
 
6.4%
0 131160
 
6.3%
a 130693
 
6.2%
4 130673
 
6.2%
c 130418
 
6.2%
1 129309
 
6.2%
Other values (6) 769439
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1309381
62.6%
Lowercase Letter 781883
37.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 134557
10.3%
2 134466
10.3%
6 134047
10.2%
3 132910
10.2%
0 131160
10.0%
4 130673
10.0%
1 129309
9.9%
5 128308
9.8%
8 128013
9.8%
7 125938
9.6%
Lowercase Letter
ValueCountFrequency (%)
b 133592
17.1%
a 130693
16.7%
c 130418
16.7%
e 129229
16.5%
f 129118
16.5%
d 128833
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1309381
62.6%
Latin 781883
37.4%

Most frequent character per script

Common
ValueCountFrequency (%)
9 134557
10.3%
2 134466
10.3%
6 134047
10.2%
3 132910
10.2%
0 131160
10.0%
4 130673
10.0%
1 129309
9.9%
5 128308
9.8%
8 128013
9.8%
7 125938
9.6%
Latin
ValueCountFrequency (%)
b 133592
17.1%
a 130693
16.7%
c 130418
16.7%
e 129229
16.5%
f 129118
16.5%
d 128833
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2091264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 134557
 
6.4%
2 134466
 
6.4%
6 134047
 
6.4%
b 133592
 
6.4%
3 132910
 
6.4%
0 131160
 
6.3%
a 130693
 
6.2%
4 130673
 
6.2%
c 130418
 
6.2%
1 129309
 
6.2%
Other values (6) 769439
36.8%

Interactions

2025-05-25T20:37:01.909467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.017595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.653160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:59.380146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.104579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.697145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.315429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.995267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.111822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-25T20:37:00.193958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.779991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.398665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:02.090975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.203355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.919830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:59.545731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.283973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.893139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.489292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:02.173295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.286217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-25T20:36:59.626380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.364906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.974696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.571052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:02.257347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.377861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:59.103535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:59.706981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.444303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.059702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.654755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:02.344242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.466421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:59.197946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:59.924646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.529932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.146317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.738282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:02.430229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:58.557598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:36:59.289872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.011532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:00.613554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.230212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T20:37:01.823951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-25T20:37:07.230239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ActAsignaturaAxiAñoCogGradoGrupoIntensidad_HorariaNivelPeriodoProcResultadoSede
Act1.0000.1120.7170.0600.494-0.1360.046-0.0150.4540.0280.5080.6730.078
Asignatura0.1121.0000.0780.3680.1680.2340.0970.7210.2150.0870.1610.1420.122
Axi0.7170.0781.0000.0800.383-0.1190.0610.0050.4100.0330.3930.5670.122
Año0.0600.3680.0801.0000.0310.1490.1870.1350.0380.3430.0330.0390.043
Cog0.4940.1680.3830.0311.000-0.1430.054-0.3350.6670.0380.9300.9450.088
Grado-0.1360.234-0.1190.149-0.1431.0000.2800.0240.0880.027-0.124-0.1480.298
Grupo0.0460.0970.0610.1870.0540.2801.0000.0480.0420.0090.0460.0520.124
Intensidad_Horaria-0.0150.7210.0050.135-0.3350.0240.0481.0000.1560.042-0.314-0.2750.045
Nivel0.4540.2150.4100.0380.6670.0880.0420.1561.0000.0390.6700.7330.096
Periodo0.0280.0870.0330.3430.0380.0270.0090.0420.0391.0000.0380.0380.000
Proc0.5080.1610.3930.0330.930-0.1240.046-0.3140.6700.0381.0000.9520.106
Resultado0.6730.1420.5670.0390.945-0.1480.052-0.2750.7330.0380.9521.0000.090
Sede0.0780.1220.1220.0430.0880.2980.1240.0450.0960.0000.1060.0901.000

Missing values

2025-05-25T20:37:02.579856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T20:37:02.748514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-25T20:37:02.931920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SedeEstudianteGradoGrupoPeriodoAñoIntensidad_HorariaAsignaturaCogProcActAxiDocenteResultadoNivelIdentificación
0FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320233CIENCIAS NATURALES95.0096.009394ALEJANDRA LEÓN DICELIS95SUPERIOR77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
1FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320233CIENCIAS SOCIALES94.0094.009595KATHERINE CUBILLOS VÉLEZ94ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
2FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320235MATEMÁTICAS74.0073.009595TATIANA GONZÁLEZ MORENO81ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
3FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320234LENGUA CASTELLANA97.0097.009595KATHERINE CUBILLOS VÉLEZ96SUPERIOR77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
4FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320234INGLÉS85.0086.009090ALEJANDRA MORALES GARCÍA87ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
5FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320232CREATIVIDAD E INNOVACIÓN94.0094.009095SANDRA SANTISTEBAN OSTOS94ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
6FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320234APRENDIZAJE BASADO EN PROYECTOS94.0094.009491ALEJANDRA LEÓN DICELIS93ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
7FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320233EDUCACIÓN FÍSICA97.0097.0010080STEVEN BARRERO BUITRAGO95SUPERIOR77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
8FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320232LECTURA CRÍTICA92.0093.009598YENNY SOTELO GÓMEZ93ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
9FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320231ARTES92.0092.009090SANDRA SANTISTEBAN OSTOS92ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
SedeEstudianteGradoGrupoPeriodoAñoIntensidad_HorariaAsignaturaCogProcActAxiDocenteResultadoNivelIdentificación
32666GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120256MATEMÁTICAS77.0080.009090XIMENA VILLANUEVA ROJAS82ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32667GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120254LENGUA CASTELLANA81.0081.008787VALENTINA SARABIA VARGAS83ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32668GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120252LECTURA CRÍTICA82.0082.009090VALENTINA SARABIA VARGAS84ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32669GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120256INGLÉS71.0074.0085100ALEXANDER VARGAS GÓMEZ79BÁSICOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32670GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120253EDUCACIÓN FÍSICA88.0084.008395FELIPE SÁNCHEZ SALDARRIAGA87ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32671GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120253INNOVACIÓN Y EMPRENDIMIENTO84.0085.008585LILIANA SARABIA VARGAS84ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32672GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120252APRENDIZAJE BASADO EN INVESTIGACIÓN83.0083.008586XIMENA VILLANUEVA ROJAS84ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32673GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120252CENTRO DE INTERÉS ARTÍSTICO94.0092.009495CENTRO DE INTERÉS94ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32674GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120251TECNOLOGÍAS INFORMÁTICAS84.0083.008080LILIANA SARABIA VARGAS82ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea
32675GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120251INTEGRALIDAD95.0095.009090STIVEN RUBIANO CAPADOR94ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17bea